Asynchronous data assimilation with the EnKF
Asynchronous data assimilation with the EnKF
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DOI：
10.1111/j.1600-0870.2009.00417.x
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发表时间：
2010-01
期刊：
影响因子：
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通讯作者：
P. Sakov;G. Evensen;Laurent Bertino
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文献类型：
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作者：
P. Sakov;G. Evensen;Laurent Bertino
摘要本文用集合卡尔曼滤波(EnKF)研究了非同步观测同化问题，即四维数据同化问题。对于一个具有完美模型和线性动力学的系统，集合卡尔曼平滑器(EnKS)提供了一个简单而有效的解决方案：人们只需要使用更新期间的集合观测(即每个集合成员的预报观测)，对于每个同化观测。这个配方既可以用于同化过去的数据，也可以用于同化未来的数据；在同化通用的异步观测的上下文中，我们将其称为异步EnKF。非同步EnKF实质上相当于四维变分资料同化(4D-Var)。它只需要一次系统的前向集成就可以获得和存储分析所需的数据，因此对于大规模应用是可行的。与4D-Var不同，异步EnKF不需要切线模型或伴随模型。
Abstract This study revisits the problem of assimilation of asynchronous observations, or four-dimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution for the problem: one just needs to use the ensemble observations (that is, the forecast observations for each ensemble member) from the time of observation during the update, for each assimilated observation. This recipe can be used for assimilating both past and future data; in the context of assimilating generic asynchronous observations we refer to it as the asynchronous EnKF. The asynchronous EnKF is essentially equivalent to the four-dimensional variational data assimilation (4D-Var). It requires only one forward integration of the system to obtain and store the data necessary for the analysis, and therefore is feasible for large-scale applications. Unlike 4D-Var, the asynchronous EnKF requires no tangent linear or adjoint model.